The IgG subclasses of platelet‐associated autoantibodies directed against platelet glycoproteins IIb/IIIa in patients with idiopathic thrombocytopenic purpura
Bibliographic record
Abstract
The majority of patients with idiopathic thrombocytopenic purpura (ITP) have antiplatelet autoantibodies that are most frequently directed against platelet glycoproteins IIb/IIIa or Ib/IX/V. However, there is some debate whether the immune response is oligoclonal or polyclonal in nature. We investigated the subclass distribution of anti-IIb/IIIa IgG autoantibodies in 59 prospectively studied patients with ITP. We also tested patients with a variety of thrombocytopenic disorders (n=31) and healthy controls (n=30). Platelet lysates were tested for IgG anti-IIb/IIIa autoantibodies, and the specific IgG subclass distribution was measured using antigen capture assays. All testing was done blinded to diagnosis and other assay results. After unblinding, we found that 43 of the 59 ITP patients had anti-IIb/IIIa autoantibodies (sensitivity=73%). Anti-IIb/IIIa autoantibodies were also detected in five of the 31 non-ITP patients, but in none of the 30 healthy controls (specificity=91%). The IgG subclass assay was positive in 39 of the 43 ITP patients with anti-IIb/IIIa antibodies (sensitivity=92%) and in 12 samples that had no detectable anti-IIb/IIIa antibodies including two ITP patients (specificity=83%). The most common subclass in the ITP patient samples was IgG1 (77%), either alone (n=14) or with other IgG subclass antibodies (n=19). However, there were also patients with only IgG2 (n=2), IgG3 (n=3) or IgG4 (n=3) antibodies. Our results are consistent with the hypothesis that ITP is a heterogeneous disorder and that some patients have evidence of oligoclonality, whereas other patients have polyclonal autoantibodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".